OpenRaiser/Intern-Atlas huggingface dataset:
Cooperative Sensing-Assisted Predictive Beam Tracking for MIMO-OFDM Networked ISAC Systems
Venue: arXiv cs.NI 2025
Year: 2025

This paper studies a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) networked integrated sensing and communication (ISAC) system, in which multiple base stations (BSs) perform beam tracking to communicate with a mobile device. In particular, we focus on the beam tracking over a number of tracking time slots (TTSs) and suppose that these BSs operate at non-overlapping frequency bands to avoid the severe inter-cell interference. Under this setup, we propose a new cooperative sensing-assisted predictive beam tracking design. In each TTS, the BSs use echo signals to cooperatively track the mobile device as a sensing target, and continuously adjust the beam directions to follow the device for enhancing the performance for both communication and sensing. First, we propose a cooperative sensing design to track the device, in which the BSs first employ the two-dimensional discrete Fourier transform (2D-DFT) technique to perform local target estimation, and then use the extended Kalman filter (EKF) method to fuse their individual measurement results for predicting the target parameters. Next, based on the predicted results, we obtain the achievable rate for communication and the predicted conditional Cramér-Rao lower bound (PC-CRLB) for target parameters estimation in the next TTS, as a function of the beamforming vectors. Accordingly, we formulate the predictive beamforming design problem, with the objective of maximizing the achievable communication rate in the following TTS, while satisfying the PC-CRLB requirement for sensing. To address the resulting non-convex problem, we first propose a semi-definite relaxation (SDR)-based algorithm to obtain the optimal solution, and then develop an alternative penalty-based algorithm to get a high-quality low-complexity solution.
LinguaSafe: A Comprehensive Multilingual Safety Benchmark for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

The widespread adoption and increasing prominence of large language models (LLMs) in global technologies necessitate a rigorous focus on ensuring their safety across a diverse range of linguistic and cultural contexts. The lack of a comprehensive evaluation and diverse data in existing multilingual safety evaluations for LLMs limits their effectiveness, hindering the development of robust multilingual safety alignment. To address this critical gap, we introduce LinguaSafe, a comprehensive multilingual safety benchmark crafted with meticulous attention to linguistic authenticity. The LinguaSafe dataset comprises 45k entries in 12 languages, ranging from Hungarian to Malay. Curated using a combination of translated, transcreated, and natively-sourced data, our dataset addresses the critical need for multilingual safety evaluations of LLMs, filling the void in the safety evaluation of LLMs across diverse under-represented languages from Hungarian to Malay. LinguaSafe presents a multidimensional and fine-grained evaluation framework, with direct and indirect safety assessments, including further evaluations for oversensitivity. The results of safety and helpfulness evaluations vary significantly across different domains and different languages, even in languages with similar resource levels. Our benchmark provides a comprehensive suite of metrics for in-depth safety evaluation, underscoring the critical importance of thoroughly assessing multilingual safety in LLMs to achieve more balanced safety alignment. Our dataset and code are released to the public to facilitate further research in the field of multilingual LLM safety.
Frequency-Driven Inverse Kernel Prediction for Single Image Defocus Deblurring
Venue: arXiv cs.CV 2025
Year: 2025

Single image defocus deblurring aims to recover an all-in-focus image from a defocus counterpart, where accurately modeling spatially varying blur kernels remains a key challenge. Most existing methods rely on spatial features for kernel estimation, but their performance degrades in severely blurry regions where local high-frequency details are missing. To address this, we propose a Frequency-Driven Inverse Kernel Prediction network (FDIKP) that incorporates frequency-domain representations to enhance structural identifiability in kernel modeling. Given the superior discriminative capability of the frequency domain for blur modeling, we design a Dual-Branch Inverse Kernel Prediction (DIKP) strategy that improves the accuracy of kernel estimation while maintaining stability. Moreover, considering the limited number of predicted inverse kernels, we introduce a Position Adaptive Convolution (PAC) to enhance the adaptability of the deconvolution process. Finally, we propose a Dual-Domain Scale Recurrent Module (DSRM) to fuse deconvolution results and progressively improve deblurring quality from coarse to fine. Extensive experiments demonstrate that our method outperforms existing approaches. Code will be made publicly available.
A Multi-Resolution Benchmark Framework for Spatial Reasoning Assessment in Neural Networks
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents preliminary results in the definition of a comprehensive benchmark framework designed to systematically evaluate spatial reasoning capabilities in neural networks, with a particular focus on morphological properties such as connectivity and distance relationships. The framework is currently being used to study the capabilities of nnU-Net, exploiting the spatial model checker VoxLogicA to generate two distinct categories of synthetic datasets: maze connectivity problems for topological analysis and spatial distance computation tasks for geometric understanding. Each category is evaluated across multiple resolutions to assess scalability and generalization properties. The automated pipeline encompasses a complete machine learning workflow including: synthetic dataset generation, standardized training with cross-validation, inference execution, and comprehensive evaluation using Dice coefficient and IoU (Intersection over Union) metrics. Preliminary experimental results demonstrate significant challenges in neural network spatial reasoning capabilities, revealing systematic failures in basic geometric and topological understanding tasks. The framework provides a reproducible experimental protocol, enabling researchers to identify specific limitations. Such limitations could be addressed through hybrid approaches combining neural networks with symbolic reasoning methods for improved spatial understanding in clinical applications, establishing a foundation for ongoing research into neural network spatial reasoning limitations and potential solutions.
Deep Research: A Survey of Autonomous Research Agents
Venue: arXiv cs.IR 2025
Year: 2025

The rapid advancement of large language models (LLMs) has driven the development of agentic systems capable of autonomously performing complex tasks. Despite their impressive capabilities, LLMs remain constrained by their internal knowledge boundaries. To overcome these limitations, the paradigm of deep research has been proposed, wherein agents actively engage in planning, retrieval, and synthesis to generate comprehensive and faithful analytical reports grounded in web-based evidence. In this survey, we provide a systematic overview of the deep research pipeline, which comprises four core stages: planning, question developing, web exploration, and report generation. For each stage, we analyze the key technical challenges and categorize representative methods developed to address them. Furthermore, we summarize recent advances in optimization techniques and benchmarks tailored for deep research. Finally, we discuss open challenges and promising research directions, aiming to chart a roadmap toward building more capable and trustworthy deep research agents.
Leveraging Diffusion Models for Stylization using Multiple Style Images
Venue: arXiv cs.CV 2025
Year: 2025

Recent advances in latent diffusion models have enabled exciting progress in image style transfer. However, several key issues remain. For example, existing methods still struggle to accurately match styles. They are often limited in the number of style images that can be used. Furthermore, they tend to entangle content and style in undesired ways. To address this, we propose leveraging multiple style images which helps better represent style features and prevent content leaking from the style images. We design a method that leverages both image prompt adapters and statistical alignment of the features during the denoising process. With this, our approach is designed such that it can intervene both at the cross-attention and the self-attention layers of the denoising UNet. For the statistical alignment, we employ clustering to distill a small representative set of attention features from the large number of attention values extracted from the style samples. As demonstrated in our experimental section, the resulting method achieves state-of-the-art results for stylization.
Reinforcement Learning with Rubric Anchors
Venue: arXiv cs.AI 2025
Year: 2025

Reinforcement Learning from Verifiable Rewards (RLVR) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs), exemplified by the success of OpenAI's o-series. In RLVR, rewards are derived from verifiable signals-such as passing unit tests in code generation or matching correct answers in mathematical reasoning. While effective, this requirement largely confines RLVR to domains with automatically checkable outcomes. To overcome this, we extend the RLVR paradigm to open-ended tasks by integrating rubric-based rewards, where carefully designed rubrics serve as structured, model-interpretable criteria for automatic scoring of subjective outputs. We construct, to our knowledge, the largest rubric reward system to date, with over 10,000 rubrics from humans, LLMs, or a hybrid human-LLM collaboration. Implementing rubric-based RL is challenging; we tackle these issues with a clear framework and present an open-sourced Qwen-30B-A3B model with notable gains: 1) With only 5K+ samples, our system improves by +5.2% on open-ended benchmarks (especially humanities), outperforming a 671B DeepSeek-V3 model by +2.4%, while preserving general and reasoning abilities. 2) Our method provides fine-grained stylistic control, using rubrics as anchors to mitigate the "AI-like" tone and produce more human-like, expressive responses. We share key lessons in rubric construction, data selection, and training, and discuss limitations and future releases.
When Alignment Hurts: Decoupling Representational Spaces in Multilingual Models
Venue: arXiv cs.CL 2025
Year: 2025

Alignment with high-resource standard languages is often assumed to aid the modeling of related low-resource varieties. We challenge this assumption by demonstrating that excessive representational entanglement with a dominant variety, such as Modern Standard Arabic (MSA) in relation to Arabic dialects, can actively hinder generative modeling. We present the first comprehensive causal study of this phenomenon by analyzing and directly intervening in the internal representation geometry of large language models (LLMs). Our key contribution is an online variational probing framework that continuously estimates the subspace of the standard variety during fine-tuning, enabling projection-based decoupling from this space. While our study uses Arabic as a case due to its unusually rich parallel resources across 25 dialects, the broader motivation is methodological: dialectal MT serves as a controlled proxy for generative tasks where comparable multi-variety corpora are unavailable. Across 25 dialects, our intervention improves generation quality by up to +4.9 chrF++ and +2.0 on average compared to standard fine-tuning, despite a measured tradeoff in standard-language performance. These results provide causal evidence that subspace dominance by high-resource varieties can restrict generative capacity for related varieties. More generally, we unify geometric and information-theoretic probing with subspace-level causal interventions, offering practical tools for improving generative modeling in closely related language families and, more broadly, for controlling representational allocation in multilingual and multi-domain LLMs. Code will be released.
Box-Reachability in Vector Addition Systems
Venue: arXiv cs.FL 2025
Year: 2025

We consider a variant of reachability in Vector Addition Systems (VAS) dubbed \emph{box reachability}, whereby a vector $v\in \mathbb{N}^d$ is box-reachable from $0$ in a VAS $V$ if $V$ admits a path from $0$ to $v$ that not only stays in the positive orthant (as in the standard VAS semantics), but also stays below $v$, i.e., within the ``box'' whose opposite corners are $0$ and $v$. Our main result is that for two-dimensional VAS, the set of box-reachable vertices almost coincides with the standard reachability set: the two sets coincide for all vectors whose coordinates are both above some threshold $W$. We also study properties of box-reachability, exploring the differences and similarities with standard reachability. Technically, our main result is proved using powerful machinery from convex geometry.
E3RG: Building Explicit Emotion-driven Empathetic Response Generation System with Multimodal Large Language Model
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal Empathetic Response Generation (MERG) is crucial for building emotionally intelligent human-computer interactions. Although large language models (LLMs) have improved text-based ERG, challenges remain in handling multimodal emotional content and maintaining identity consistency. Thus, we propose E3RG, an Explicit Emotion-driven Empathetic Response Generation System based on multimodal LLMs which decomposes MERG task into three parts: multimodal empathy understanding, empathy memory retrieval, and multimodal response generation. By integrating advanced expressive speech and video generative models, E3RG delivers natural, emotionally rich, and identity-consistent responses without extra training. Experiments validate the superiority of our system on both zero-shot and few-shot settings, securing Top-1 position in the Avatar-based Multimodal Empathy Challenge on ACM MM 25. Our code is available at https://github.com/RH-Lin/E3RG.
Evaluating the Quality of Open Building Datasets for Mapping Urban Inequality: A Comparative Analysis Across 5 Cities
Venue: arXiv cs.DB 2025
Year: 2025

While informal settlements lack focused development and are highly dynamic, the quality of spatial data for these places may be uncertain. This study evaluates the quality and biases of AI-generated Open Building Datasets (OBDs) generated by Google and Microsoft against OpenStreetMap (OSM) data, across diverse global cities including Accra, Nairobi, Caracas, Berlin, and Houston. The Intersection over Union (IoU), overlap analysis and a positional accuracy algorithm are used to analyse the similarity and alignment of the datasets. The paper also analyses the size distribution of the building polygon area, and completeness using predefined but regular spatial units. The results indicate significant variance in data quality, with Houston and Berlin demonstrating high alignment and completeness, reflecting their structured urban environments. There are gaps in the datasets analysed, and cities like Accra and Caracas may be under-represented. This could highlight difficulties in capturing complex or informal regions. The study also notes different building size distributions, which may be indicative of the global socio-economic divide. These findings may emphasise the need to consider the quality of global building datasets to avoid misrepresentation, which is an important element of planning and resource distribution.
SecFSM: Knowledge Graph-Guided Verilog Code Generation for Secure Finite State Machines in Systems-on-Chip
Venue: arXiv cs.CR 2025
Year: 2025

Finite State Machines (FSMs) play a critical role in implementing control logic for Systems-on-Chip (SoC). Traditionally, FSMs are implemented by hardware engineers through Verilog coding, which is often tedious and time-consuming. Recently, with the remarkable progress of Large Language Models (LLMs) in code generation, LLMs have been increasingly explored for automating Verilog code generation. However, LLM-generated Verilog code often suffers from security vulnerabilities, which is particularly concerning for security-sensitive FSM implementations. To address this issue, we propose SecFSM, a novel method that leverages a security-oriented knowledge graph to guide LLMs in generating more secure Verilog code. Specifically, we first construct a FSM Security Knowledge Graph (FSKG) as an external aid to LLMs. Subsequently, we analyze users' requirements to identify vulnerabilities and get a list of vulnerabilities in the requirements. Then, we retrieve knowledge from FSKG based on the vulnerabilities list. Finally, we construct security prompts based on the security knowledge for Verilog code generation. To evaluate SecFSM, we build a dedicated dataset collected from academic datasets, artificial datasets, papers, and industrial cases. Extensive experiments demonstrate that SecFSM outperforms state-of-the-art baselines. In particular, on a benchmark of 25 security test cases evaluated by DeepSeek-R1, SecFSM achieves an outstanding pass rate of 21/25.
Deformation of the panoramic sphere into an ellipsoid to induce self-motion in telepresence users
Venue: arXiv cs.RO 2025
Year: 2025

Mobile telepresence robots allow users to feel present and explore remote environments using technology. Traditionally, these systems are implemented using a camera onboard a mobile robot that can be controlled. Although high-immersion technologies, such as 360-degree cameras, can increase situational awareness and presence, they also introduce significant challenges. Additional processing and bandwidth requirements often result in latencies of up to seconds. The current delay with a 360-degree camera streaming over the internet makes real-time control of these systems difficult. Working with high-latency systems requires some form of assistance to the users. This study presents a novel way to utilize optical flow to create an illusion of self-motion to the user during the latency period between user sending motion commands to the robot and seeing the actual motion through the 360-camera stream. We find no significant benefit of using the self-motion illusion to performance or accuracy of controlling a telepresence robot with a latency of 500 ms, as measured by the task completion time and collisions into objects. Some evidence is shown that the method might increase virtual reality (VR) sickness, as measured by the simulator sickness questionnaire (SSQ). We conclude that further adjustments are necessary in order to render the method viable.
SEDEG:Sequential Enhancement of Decoder and Encoder's Generality for Class Incremental Learning with Small Memory
Venue: arXiv cs.CV 2025
Year: 2025

In incremental learning, enhancing the generality of knowledge is crucial for adapting to dynamic data inputs. It can develop generalized representations or more balanced decision boundaries, preventing the degradation of long-term knowledge over time and thus mitigating catastrophic forgetting. Some emerging incremental learning methods adopt an encoder-decoder architecture and have achieved promising results. In the encoder-decoder achitecture, improving the generalization capabilities of both the encoder and decoder is critical, as it helps preserve previously learned knowledge while ensuring adaptability and robustness to new, diverse data inputs. However, many existing continual methods focus solely on enhancing one of the two components, which limits their effectiveness in mitigating catastrophic forgetting. And these methods perform even worse in small-memory scenarios, where only a limited number of historical samples can be stored. To mitigate this limitation, we introduces SEDEG, a two-stage training framework for vision transformers (ViT), focusing on sequentially improving the generality of both Decoder and Encoder. Initially, SEDEG trains an ensembled encoder through feature boosting to learn generalized representations, which subsequently enhance the decoder's generality and balance the classifier. The next stage involves using knowledge distillation (KD) strategies to compress the ensembled encoder and develop a new, more generalized encoder. This involves using a balanced KD approach and feature KD for effective knowledge transfer. Extensive experiments on three benchmark datasets show SEDEG's superior performance, and ablation studies confirm the efficacy of its components. The code is available at https://github.com/ShaolingPu/CIL.
OPTIC-ER: A Reinforcement Learning Framework for Real-Time Emergency Response and Equitable Resource Allocation in Underserved African Communities
Venue: arXiv cs.AI 2025
Year: 2025

Public service systems in many African regions suffer from delayed emergency response and spatial inequity, causing avoidable suffering. This paper introduces OPTIC-ER, a reinforcement learning (RL) framework for real-time, adaptive, and equitable emergency response. OPTIC-ER uses an attention-guided actor-critic architecture to manage the complexity of dispatch environments. Its key innovations are a Context-Rich State Vector, encoding action sub-optimality, and a Precision Reward Function, which penalizes inefficiency. Training occurs in a high-fidelity simulation using real data from Rivers State, Nigeria, accelerated by a precomputed Travel Time Atlas. The system is built on the TALS framework (Thin computing, Adaptability, Low-cost, Scalability) for deployment in low-resource settings. In evaluations on 500 unseen incidents, OPTIC-ER achieved a 100.00% optimal action selection rate, confirming its robustness and generalization. Beyond dispatch, the system generates Infrastructure Deficiency Maps and Equity Monitoring Dashboards to guide proactive governance and data-informed development. This work presents a validated blueprint for AI-augmented public services, showing how context-aware RL can bridge the gap between algorithmic decision-making and measurable human impact.
SL-ACC: A Communication-Efficient Split Learning Framework with Adaptive Channel-wise Compression
Venue: arXiv cs.LG 2025
Year: 2025

The increasing complexity of neural networks poses a significant barrier to the deployment of distributed machine learning (ML) on resource-constrained devices, such as federated learning (FL). Split learning (SL) offers a promising solution by offloading the primary computing load from edge devices to a server via model partitioning. However, as the number of participating devices increases, the transmission of excessive smashed data (i.e., activations and gradients) becomes a major bottleneck for SL, slowing down the model training. To tackle this challenge, we propose a communication-efficient SL framework, named SL-ACC, which comprises two key components: adaptive channel importance identification (ACII) and channel grouping compression (CGC). ACII first identifies the contribution of each channel in the smashed data to model training using Shannon entropy. Following this, CGC groups the channels based on their entropy and performs group-wise adaptive compression to shrink the transmission volume without compromising training accuracy. Extensive experiments across various datasets validate that our proposed SL-ACC framework takes considerably less time to achieve a target accuracy than state-of-the-art benchmarks.